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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - GPU-accelerated data manipulation using cuDF 1. Groupby, apply, and aggregation operations 2. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage
Machine Learning	15%	- Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Training models using cuML and GPU-accelerated XGBoost 3. Multi-GPU training strategies - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Hyperparameter tuning techniques 3. Batching and memory-efficient training methods
Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation
Data Analysis	14%	- Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets
GPU and Cloud Computing	16%	- Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q12-Q17):

NEW QUESTION # 12
A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A. Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
B. The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
C. The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.
D. cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.

Answer: C

NEW QUESTION # 13
You are working on a data science project that requires augmenting a dataset using synthetic data.
You are utilizing cuDF and NVIDIA RAPIDS to speed up the data generation process.
Which of the following methods is the most effective way to generate synthetic data using cuDF in a RAPIDS workflow?

A. Use cudf.DataFrame.sample() to duplicate random rows and create synthetic data.
B. Use cuDF to manipulate data distributions and generate new data points based on existing features.
C. Use cudf.DataFrame.applymap() to create new synthetic features through complex mathematical functions.

Answer: B

NEW QUESTION # 14
A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A. Disabling cuDNN optimizations to allow more flexibility in kernel execution
B. Increasing the batch size beyond the GPU's memory limit
C. Using only CPU-based data augmentation to keep the GPU dedicated to training
D. Using mixed precision training with NVIDIA Tensor Cores

Answer: D

NEW QUESTION # 15
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('int64')
B. df['item_id'] = df['item_id'].astype('int32')
C. df['item_id'] = df['item_id'].astype('float64')
D. df['item_id'] = df['item_id'].astype('string')

Answer: A

NEW QUESTION # 16
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
B. Running the model in a local Python virtual environment and copying dependencies manually
C. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
D. Compiling all dependencies into the host machine and using system-wide installations

Answer: C

NEW QUESTION # 17
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